Many geometric parameters related to cardiac chambers of a patient are of interest to clinicians. Specific examples of such geometric parameters include surface coordinates and cavity volume at specific moments in the cardiac cycle, ejection fraction and phase differences between data relating to two distinct cardiac chambers or to different parts of a specific cardiac chamber, among others. Many of these parameters can be estimated further to the computation of a suitable model of a surface of one or more cardiac chambers. Typically, the model includes a plurality of surface coordinate tri-dimensional vectors.
While the present document refers to cardiac chambers of a patient, it is to be understood that the patient is not necessarily a human suffering from a pathology or from symptoms of a pathology. For example, the patient could be a normal non-pathologic human subject undergoing a preventive diagnostic procedure, an athlete undergoing a study aiming to improve athletic performance, a human subject participating in a research protocol or an animal, among others. However, for brevity, it will be assumed herein after that the patient is a human subject who is not necessarily suffering from a pathology.
A specific example of a prior art method for computing a model of the surface of a cardiac chamber involves the segmentation of a cardiac tomography data set, the cardiac tomography data set including a plurality of voxels. A voxel is an equivalent of a pixel for multi-dimensional data. Accordingly, a voxel includes an intensity value and a tri-dimensional position vector. An objective of the segmentation process is to delimitate physically significant regions in the cardiac tomography data set. In the case discussed herein, segmentation is directed to the delimitation of a cardiac chamber of interest through the computation of a model of a surface of interest, the surface of interest delimitating the cardiac chamber of interest. Many methods for computing a model of the surface of the cardiac chamber of interest from a cardiac tomography data set have been proposed, but they are mostly unsatisfactory.
In a first prior art method for computing a model of the surface of the cardiac chamber, a user manually identifies regions of interest on bi-dimensional slices in the cardiac tomography data set. The regions of interest are regions on each bi-dimensional slice corresponding to the cardiac chamber of interest. The model of the surface of the cardiac chamber of interest includes voxel coordinates of the voxels peripheral to the voxels selected on each slice. A disadvantage of this method resides in a strong dependency on the user. Accordingly, the models of the surface of the cardiac chamber of interest produced by different users will not be identical and are highly likely to be imprecise.
In a second method for computing a model of the surface of the cardiac chamber, referred to as the Germano-type method and described in detail in Van Kriekinge et al., Automatic quantification of left ventricular ejection fraction from gated blood pool SPECT, Journal of Nuclear Cardiology, 1999, Vol. 6, pp 498-506, an ellipsoid is fitted to voxels from the cardiac tomography data set representative of a cardiac chamber of interest. The cardiac tomography data set is acquired using a single photon emission computed tomography (SPECT) imaging apparatus further to an injection in a patient of a radioactive substance. The general location of the cardiac chamber of interest, and therefore of the voxels used for the fit, can be identified in the cardiac tomography data set by a user or through the use of heuristic methods, among others.
Subsequently, a plurality of biopsy data sets are computed. Each biopsy data set includes a plurality of data points and is representative of a plurality of voxels oriented in a direction of a respective ray originating at a centre of the ellipsoid. Each biopsy data set is numerically differentiated once and twice to provide respective first and second derivatives data sets. Then, a complex algorithm is used to fit a surface to candidate surface points computed from the first derivative data set. Since it has been shown that the surface of the cardiac chamber is located on a given profile at a location close to a minimum of the first derivative data set, each candidate data points is located at a minimum of a respective first derivative data set. Subsequently, the fitting procedure is used to fit a surface to the candidate data points, each candidate data point having a weight depending on a local maximum of the second derivative data set. Further details regarding the fitting procedure can be found in the above-referenced article by Van Kriekinge et al.
The Germano-type method presents two major deficiencies. First, since the ventricle is not an ellipsoid, some rays will not be perpendicular to the real surface of the cavity to model. Therefore, uncertainties in the exact location of the surface will be introduced in the model of the surface of the cardiac chamber of interest. In addition, since the exact location of the surface is not necessarily at a minimum of the first derivative data set it is very likely that errors are incorporated in the model of the surface of the cardiac chamber of interest.
In conclusion, currently available models of the surface of interest are error-prone due to deficiencies inherent to the segmentation methods currently used. Accordingly, any parameter estimated from these models will also be erroneous. Therefore, there is a need in the industry to provide novel methods and apparatuses to compute a geometric parameter of a cardiac chamber from a cardiac tomography data set.
In addition, a clinician might be interested in gaining information related to a synchronization of contraction between different portions of a heart. For example, a criterion used currently by clinicians to indicate a lack of synchronization between a contraction of a left ventricle and a contraction of a right ventricle is the presence of a widened QRS complex in an electrocardiogram. However, many other causes of such a widened QRS complex exist, which makes this criterion non-specific.
Accordingly, there is a need in the industry to provide novel methods and apparatuses to measure a synchronization of contraction between different portions of a heart.
The present description refers to a number of documents, the content of which is herein incorporated by reference in their entirety.